anything-llm/server/utils/AiProviders/openAi/index.js

const { toChunks } = require("../../helpers");

class OpenAi {
  constructor() {
    const { Configuration, OpenAIApi } = require("openai");
    const config = new Configuration({
      apiKey: process.env.OPEN_AI_KEY,
    });
    const openai = new OpenAIApi(config);
    this.openai = openai;

    // Arbitrary limit to ensure we stay within reasonable POST request size.
    this.embeddingChunkLimit = 1_000;
  }

  isValidChatModel(modelName = "") {
    const validModels = ["gpt-4", "gpt-3.5-turbo"];
    return validModels.includes(modelName);
  }

  async isSafe(input = "") {
    const { flagged = false, categories = {} } = await this.openai
      .createModeration({ input })
      .then((json) => {
        const res = json.data;
        if (!res.hasOwnProperty("results"))
          throw new Error("OpenAI moderation: No results!");
        if (res.results.length === 0)
          throw new Error("OpenAI moderation: No results length!");
        return res.results[0];
      })
      .catch((error) => {
        throw new Error(
          `OpenAI::CreateModeration failed with: ${error.message}`
        );
      });

    if (!flagged) return { safe: true, reasons: [] };
    const reasons = Object.keys(categories)
      .map((category) => {
        const value = categories[category];
        if (value === true) {
          return category.replace("/", " or ");
        } else {
          return null;
        }
      })
      .filter((reason) => !!reason);

    return { safe: false, reasons };
  }

  async sendChat(chatHistory = [], prompt, workspace = {}) {
    const model = process.env.OPEN_MODEL_PREF;
    if (!this.isValidChatModel(model))
      throw new Error(
        `OpenAI chat: ${model} is not valid for chat completion!`
      );

    const textResponse = await this.openai
      .createChatCompletion({
        model,
        temperature: Number(workspace?.openAiTemp ?? 0.7),
        n: 1,
        messages: [
          { role: "system", content: "" },
          ...chatHistory,
          { role: "user", content: prompt },
        ],
      })
      .then((json) => {
        const res = json.data;
        if (!res.hasOwnProperty("choices"))
          throw new Error("OpenAI chat: No results!");
        if (res.choices.length === 0)
          throw new Error("OpenAI chat: No results length!");
        return res.choices[0].message.content;
      })
      .catch((error) => {
        console.log(error);
        throw new Error(
          `OpenAI::createChatCompletion failed with: ${error.message}`
        );
      });

    return textResponse;
  }

  async getChatCompletion(messages = [], { temperature = 0.7 }) {
    const model = process.env.OPEN_MODEL_PREF || "gpt-3.5-turbo";
    const { data } = await this.openai.createChatCompletion({
      model,
      messages,
      temperature,
    });

    if (!data.hasOwnProperty("choices")) return null;
    return data.choices[0].message.content;
  }

  async embedTextInput(textInput) {
    const result = await this.embedChunks(textInput);
    return result?.[0] || [];
  }

  async embedChunks(textChunks = []) {
    // Because there is a hard POST limit on how many chunks can be sent at once to OpenAI (~8mb)
    // we concurrently execute each max batch of text chunks possible.
    // Refer to constructor embeddingChunkLimit for more info.
    const embeddingRequests = [];
    for (const chunk of toChunks(textChunks, this.embeddingChunkLimit)) {
      embeddingRequests.push(
        new Promise((resolve) => {
          this.openai
            .createEmbedding({
              model: "text-embedding-ada-002",
              input: chunk,
            })
            .then((res) => {
              resolve({ data: res.data?.data, error: null });
            })
            .catch((e) => {
              resolve({ data: [], error: e?.error });
            });
        })
      );
    }

    const { data = [], error = null } = await Promise.all(
      embeddingRequests
    ).then((results) => {
      // If any errors were returned from OpenAI abort the entire sequence because the embeddings
      // will be incomplete.
      const errors = results
        .filter((res) => !!res.error)
        .map((res) => res.error)
        .flat();
      if (errors.length > 0) {
        return {
          data: [],
          error: `(${errors.length}) Embedding Errors! ${errors
            .map((error) => `[${error.type}]: ${error.message}`)
            .join(", ")}`,
        };
      }
      return {
        data: results.map((res) => res?.data || []).flat(),
        error: null,
      };
    });

    if (!!error) throw new Error(`OpenAI Failed to embed: ${error}`);
    return data.length > 0 &&
      data.every((embd) => embd.hasOwnProperty("embedding"))
      ? data.map((embd) => embd.embedding)
      : null;
  }
}

module.exports = {
  OpenAi,
};